Artificial neural network‐based quantitative structure–activity relationships model and molecular docking for virtual screening of novel potent acetylcholinesterase inhibitors

Thiadiazine-thiones as inhibitors of leishmania pteridine reductase (PTR1) target: investigations and in silico approach

Leishmaniasis is a widespread parasitic disease and is one of the major public health concerns in developing countries. Many drugs have been identified for leishmania as targets, but the potential toxicity and long-term treatment remain the most significant problems in terms of further development. The present study employed physicochemical investigations, structure-based virtual screening, ADMET analysis, molecular dynamics simulation, and MM-PBSA, to identify potential compounds for Leishmania. We evaluated 30,926 natural products from the NPASS database, and four potentials passed the pharmacokinetic ADMET studies and were verified using the molecular docking approach. Molecular docking results showed good binding interaction of the compounds with the active site of leishmania pteridine reductase enzyme PTR1, with compound TTC1 showing FRED and Autodock binding energies of -10.33 and -10.94, respectively, which were comparable with the antileishmania drugs of Allopurinol, Miltefosine and the original ligand, methotrexate. TTC1 compound was found to be favorable for hydrophobic interaction with PTR1. In addition, the physicochemical properties of the compounds were studied using the SwissADME web server. All compounds followed Lipinski's rule of five and can be considered as good oral candidates. The analysis of the 100 ns molecular dynamics simulation results based on the best-docked TTC1 with PTR1 receptor demonstrates stable interactions, and the complex undergoes low conformational fluctuations. The average of the calculated binding free energy of the TTC1-1e7w complex is (-68.67 kJ/mol), and the result demonstrated that the TTC1 promoted stability to the Leishmania-PTR1 complex. The potential compounds can be further explored for their antileishmanial activity.Communicated by Ramaswamy H. Sarma.

Structure‐Based Approaches for the Prediction of Alzheimer's Disease Inhibitors: Comparative Interactions Analysis, Pharmacophore Modeling and Molecular Dynamics Simulations

Due to its significant role in neurodegeneration, Cyclin‐dependent kinase 5 (CDK5) has emerged as a potential target for addressing neuropathological disorders, including Alzheimer's disease (AD). The application of CDK5 inhibitors has demonstrated promise in the treatment of AD. This prompted us to model this interesting target using a computational workflow named Docking‐based Comparative Intermolecular Contacts Analysis (dbCICA). Approaches including 3D‐QSAR, genetic algorithm, and pharmacophore modeling were employed to discover new CDK inhibitors.

Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine Sulfonamides as an Important Scaffold for Anticancer Drug Discovery-In Vitro and In Silico Evaluation

Pyrazolo[4,3-e]tetrazolo[1,5-b][1,2,4]triazine sulfonamides (MM-compounds) are a relatively new class of heterocyclic compounds that exhibit a wide variety of biological actions, including anticancer properties. Here, we used caspase enzyme activity assays, flow cytometry analysis of propidium iodide (PI)-stained cells, and a DNA laddering assay to investigate the mechanisms of cell death triggered by the MM-compounds (MM134, -6, -7, and -9). Due to inconsistent results in caspase activity assays, we have performed a bromodeoxyuridine (BrdU) incorporation assay, colony formation assay, and gene expression profiling. The compounds' cytotoxic and pro-oxidative properties were also assessed. Additionally, computational studies were performed to demonstrate the potential of the scaffold for future drug discovery endeavors. MM-compounds exhibited strong micromolar (0.06-0.35 µM) anti-proliferative and pro-oxidative activity in two cancer cell lines (BxPC-3 and PC-3). Activation of caspase 3/7 was observed following a 24-h treatment of BxPC-3 cells with IC₅₀ concentrations of MM134, -6, and -9 compounds. However, no DNA fragmentation characteristics for apoptosis were observed in the flow cytometry and DNA laddering analysis. Gene expression data indicated up-regulation of BCL10, GADD45A, RIPK2, TNF, TNFRSF10B, and TNFRSF1A (TNF-R1) following treatment of cells with the MM134 compound. Moreover, in silico studies indicated AKT2 kinase as the primary target of compounds. MM-compounds exhibit strong cytotoxic activity with pro-oxidative, pro-apoptotic, and possibly pro-necroptotic properties that could be employed for further drug discovery approaches.

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.

Synthesis, molecular docking and extensive structure activity relationship of substituted DHP derivatives: a new class of herbicides

In the present study, twenty-two derivatives of dihydropyridine (DHP) have been synthesized using the Boric acid catalyst in solventless conditions. The synthesis was confirmed by FTIR analysis, 1HNMR, and 13CNMR analysis. The quantitative structure-activity relationship for all the synthesized derivatives was performed using an artificial neural network with correlation coefficient (R²) 0.8611, mean standard error 0.19, and Comparative molecular field analysis (CoMFA) with correlation coefficient (R²) 0.713, mean standard error 0.27. The molecular docking activity of synthesized compounds was tested using "AUTODOCK VINA" against "Acetohydroxyacid synthase protein receptors (PDB code 1YHZ)" acquired from the "RCSB Protein Data Bank". Docking experiments demonstrated favorable interaction among synthesized DHP derivatives and protein receptors with significant binding energy values. These synthesized derivatives have been screened for their pre-emergence herbicidal bioassay against weed species Echinochola crus galli, and the IC50 value were calculated and activity was compared with Butachlor, significant activity was exhibited by all the derivatives. All the synthesized compounds were also screened for their post emergence herbicidal activity against Echinochola crus galli, and the activity of DHPs were compared with penoxulum. All the synthesized compounds show good to moderate activity. Thus, it is concluded that substituted DHP derivatives may be developed as potential herbicides.